Retrieval-Enhanced Visual Prompt Learning for Few-shot Classification

Prompt learning has become a popular approach for adapting large vision-language models, such as CLIP, to downstream tasks. Typically, prompt learning relies on a fixed prompt token or an input-conditional token to fit a small amount of data under full supervision. While this paradigm can generalize to a certain range of unseen classes, it may struggle when domain gap increases, such as in fine-grained classification and satellite image segmentation. To address this limitation, we propose Retrieval-enhanced Prompt learning (RePrompt), which introduces retrieval mechanisms to cache the knowledge representations from downstream tasks. we first construct a retrieval database from training examples, or from external examples when available. We then integrate this retrieval-enhanced mechanism into various stages of a simple prompt learning baseline. By referencing similar samples in the training set, the enhanced model is better able to adapt to new tasks with few samples. Our extensive experiments over 15 vision datasets, including 11 downstream tasks with few-shot setting and 4 domain generalization benchmarks, demonstrate that RePrompt achieves considerably improved performance. Our proposed approach provides a promising solution to the challenges faced by prompt learning when domain gap increases. The code and models will be available

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Low-dose interleukin-2 and regulatory T cell treatments attenuate punctate and dynamic mechanical allodynia in a mouse model of sciatic nerve injury

Purpose: Nerve injury-induced mechanical hyper-sensitivity, in particular stroking-induced dynamic allodynia, is highly debilitating and difficult to treat. Previous studies indicate that the immunosuppressive regulatory T (Treg) cells modulate the magnitude of punctate mechanical allodynia resulting from sciatic nerve injury. However, whether enhancing Treg-mediated suppression attenuates dynamic allodynia is not known. In the present study, we addressed this knowledge gap by treating mice with low-dose interleukin-2 (ld-IL2) injections or adoptive transfer of Treg cells. Methods: Female Swiss Webster mice received daily injections of ld-IL2 (1 μg/mouse, intraperitoneally) either before or after unilateral spared nerve injury (SNI). Male C57BL/6J mice received adoptive transfer of 1 x 10 Results: Ld-IL2 pretreatment in female Swiss Webster mice completely blocked the development of SNI-induced dynamic mechanical allodynia and reduced the magnitude of punctate allodynia. Delayed ld-IL2 treatment in female mice significantly attenuated the morphine-resistant punctate and dynamic allodynia at 3-5 weeks post-SNI. Adoptive transfer of Treg cells to male C57BL/6J mice 3 weeks post-SNI effectively reversed the persistent punctate and dynamic allodynia, supporting that the effect of ld-IL2 is mediated through endogenous Treg cells, and is likely independent of mouse strain and sex. Neither ld-IL2 treatment nor Treg transfer affected the basal responses to punctate or brush stimuli. Ld-IL2 significantly increased the frequency of Treg cells among total CD3 Conclusion: Collectively, results from the present study supports Treg as a cellular target and ld-IL2 as a potential therapeutic option for nerve injury-induced persistent punctate and dynamic mechanical allodynia

Efficient Re-parameterization Operations Search for Easy-to-Deploy Network Based on Directional Evolutionary Strategy

Structural re-parameterization (Rep) methods has achieved significant performance improvement on traditional convolutional network. Most current Rep methods rely on prior knowledge to select the reparameterization operations. However, the performance of architecture is limited by the type of operations and prior knowledge. To break this restriction, in this work, an improved re-parameterization search space is designed, which including more type of re-parameterization operations. Concretely, the performance of convolutional networks can be further improved by the search space. To effectively explore this search space, an automatic re-parameterization enhancement strategy is designed based on neural architecture search (NAS), which can search a excellent re-parameterization architecture. Besides, we visualize the output features of the architecture to analyze the reasons for the formation of the re-parameterization architecture. On public datasets, we achieve better results. Under the same training conditions as ResNet, we improve the accuracy of ResNet-50 by 1.82% on ImageNet-1k.Comment: 21pages, 8figure

Modelling Hydration Swelling and Weakening of Montmorillonite Particles in Mudstone

It is of paramount importance to understand the hydration swelling and weakening properties of clay minerals, such as montmorillonite, to determine their mechanical responses during deep underground argillaceous engineering. In this study, the mineral components and microscopic structure of mudstone were characterised using X-ray powder diffraction and field-emission scanning electron microscopy. Experimental schemes were devised to determine the properties of mudstone under the influence of underground water and stress; these involved compacting montmorillonite particles with various water contents and conducting uniaxial compression tests. Experimental results demonstrated that compaction stress changes the microscopic structure of the montmorillonite matrix and affects its properties, and stress independency was found at particular water and stress conditions. Two equations were then obtained to describe the swelling and weakening properties of the montmorillonite matrix based on the discrete element method; further, the hydration swelling equation represents the linear decrease in the density of the montmorillonite matrix with an increase in the water content. It was also determined that the water dependency of uniaxial compressive strength can be described by negative quartic equations, and the uniaxial compressive strength of the montmorillonite matrix is just 0.04 MPa with a water content of 0.6. The experimental results are in good agreement with the calculated solutions and provide an important experimental basis to the understanding of the mechanical properties of montmorillonite-rich mudstones under the influence of underground water and stress

A spine segmentation method based on scene aware fusion network

Abstract Background Intervertebral disc herniation, degenerative lumbar spinal stenosis, and other lumbar spine diseases can occur across most age groups. MRI examination is the most commonly used detection method for lumbar spine lesions with its good soft tissue image resolution. However, the diagnosis accuracy is highly dependent on the experience of the diagnostician, leading to subjective errors caused by diagnosticians or differences in diagnostic criteria for multi-center studies in different hospitals, and inefficient diagnosis. These factors necessitate the standardized interpretation and automated classification of lumbar spine MRI to achieve objective consistency. In this research, a deep learning network based on SAFNet is proposed to solve the above challenges. Methods In this research, low-level features, mid-level features, and high-level features of spine MRI are extracted. ASPP is used to process the high-level features. The multi-scale feature fusion method is used to increase the scene perception ability of the low-level features and mid-level features. The high-level features are further processed using global adaptive pooling and Sigmoid function to obtain new high-level features. The processed high-level features are then point-multiplied with the mid-level features and low-level features to obtain new high-level features. The new high-level features, low-level features, and mid-level features are all sampled to the same size and concatenated in the channel dimension to output the final result. Results The DSC of SAFNet for segmenting 17 vertebral structures among 5 folds are 79.46 ± 4.63%, 78.82 ± 7.97%, 81.32 ± 3.45%, 80.56 ± 5.47%, and 80.83 ± 3.48%, with an average DSC of 80.32 ± 5.00%. The average DSC was 80.32 ± 5.00%. Compared to existing methods, our SAFNet provides better segmentation results and has important implications for the diagnosis of spinal and lumbar diseases. Conclusions This research proposes SAFNet, a highly accurate and robust spine segmentation deep learning network capable of providing effective anatomical segmentation for diagnostic purposes. The results demonstrate the effectiveness of the proposed method and its potential for improving radiological diagnosis accuracy

Targeting of N-Type Calcium Channels via GABAB-Receptor Activation by α-Conotoxin Vc1.1 Variants Displaying Improved Analgesic Activity

α-Conotoxins exhibiting analgesic activity, such as Vc1.1, have been shown to inhibit α9α10 nicotinic acetylcholine receptors (nAChRs) and GABAB-receptor (GABABR) coupled N-type (CaV2.2) calcium channels. Here, we report two Vc1.1 variants, Vc1.1[N9R] and benzoyl-Vc1.1[N9R], that selectively inhibit CaV2.2 channels via GABABR activation but exhibit reduced inhibitory activity at α9α10 and other neuronal nAChR subtypes compared with Vc1.1. Surprisingly, the analgesic activity of Vc1.1[N9R] and benzoyl-Vc1.1[N9R] was more potent than that of Vc1.1 when tested in partial sciatic nerve ligation injury and chronic constriction injury models. Vc1.1[N9R] and benzoyl-Vc1.1[N9R] exhibited either similar or tenfold higher activity of GABABR-mediated CaV2.2 inhibition but no activity at CaV2.2 alone; however, the mechanism of increased analgesic activity is unknown. The effects on analgesic activity and α9α10 nAChR of other Vc1.1 variations at position 9 and the N-terminus were also determined. Our findings provide new insights for designing potent inhibitors for GABABR-coupled N-type (CaV2.2) calcium channels